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WO1998018264A1 - Circuit pour le decodage couleur d'un signal video - Google Patents

Circuit pour le decodage couleur d'un signal video Download PDF

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Publication number
WO1998018264A1
WO1998018264A1 PCT/DE1997/002374 DE9702374W WO9818264A1 WO 1998018264 A1 WO1998018264 A1 WO 1998018264A1 DE 9702374 W DE9702374 W DE 9702374W WO 9818264 A1 WO9818264 A1 WO 9818264A1
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WO
WIPO (PCT)
Prior art keywords
color
video signal
input
neural network
signal component
Prior art date
Application number
PCT/DE1997/002374
Other languages
German (de)
English (en)
Inventor
Stefan Prange
Michael Jansen
Joachim Horn
Original Assignee
Siemens Aktiengesellschaft
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Siemens Aktiengesellschaft filed Critical Siemens Aktiengesellschaft
Publication of WO1998018264A1 publication Critical patent/WO1998018264A1/fr

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N9/00—Details of colour television systems
    • H04N9/77—Circuits for processing the brightness signal and the chrominance signal relative to each other, e.g. adjusting the phase of the brightness signal relative to the colour signal, correcting differential gain or differential phase
    • H04N9/78—Circuits for processing the brightness signal and the chrominance signal relative to each other, e.g. adjusting the phase of the brightness signal relative to the colour signal, correcting differential gain or differential phase for separating the brightness signal or the chrominance signal from the colour television signal, e.g. using comb filter
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T9/00—Image coding
    • G06T9/002—Image coding using neural networks

Definitions

  • the invention relates to a circuit arrangement for color decoding a video signal, by means of which the brightness and color signal components contained in the video signal can be generated separately from one another.
  • the color information of a video signal is known to be arranged in the upper part of its frequency spectrum modulated on the color carrier.
  • the video signal is therefore synchronously demodulated with orthogonal color carrier components.
  • Comb filtering is used in more recent color decoders, as described in EP-0 471 700 A.
  • Input pixels are fed to the comb filter from an input mask which comprises three lines in the vertical direction and a few pixels in the horizontal direction.
  • different filtering methods two- and one-line comb filtering as well as simple decoding
  • the filtering methods are parameterized based on empirical values.
  • the classic method provides crosstalk between chrominance and luminance, in particular in the case of spatially high-frequency structures, e.g. vertical jumps in brightness.
  • adaptive comb filtering the image conditions are taken into account by hard switching between different methods.
  • the method does not provide optimal results for all image content that occurs in practice.
  • the object of the invention is to provide a circuit arrangement for color decoding a video signal, which enables a good quality color decoding even with different picture contents. According to the invention, this object is achieved by a circuit arrangement for color decoding according to the features of patent claim 1.
  • a neural network acts as a nonlinear filter. By “training” the network, whereby the available parameters of the network are set, a desired filter function is obtained. When training the neural network in the color decoder, the color decoding is also selected
  • Training images numerically optimized based on a specified quality criterion. Even if the image data to be processed by the already trained color decoder in later practical operation deviate from the training images, the neural network delivers good decoding results. In addition, the network can be specially trained for particularly critical image content.
  • a video signal is applied to the neural network on the input side with a sequence of training images in which uminance and chrominance are combined in the signal as described at the beginning.
  • the associated ideal decoded signals are already known. For example, original images are assumed for which the training video signal or the decoded signals have been calculated in a different way.
  • the - defective - output image calculated by the color decoder is compared with the ideal reference output image.
  • the determined error is used to correct the adjustable parameters of the network in such a way that the error subsequently becomes smaller.
  • the known method of gradual gradient descent by means of error back propagation is preferably used. Step-by-step training leads to weight parameters of the neural network that are not too large and relatively evenly distributed. This ensures a good approximation of the decoder output.
  • a suitable termination criterion during training ⁇ forms a stagnant course of the sum of squares weighting factor and squares.
  • a stochastic training is also expedient, in which the input data for each training step does not consist of the complete image content, but of a random image section.
  • the section changes constantly between the iterative training steps of error back propagation. This avoids secondary minima of the error and ensures that the training process settles safely to a relevant minimum of errors.
  • the optimization of the network structure which results during the training and the values of the weight factors are then adopted as the dimensioning of the network for mass production, for example as an integrated circuit.
  • the network can be implemented as an application-specific hardware circuit or processor-supported with appropriate software control.
  • the abundance of training data is almost unlimited. It has been found to be advantageous that the training data for optimal training contain as many edge structures as possible with different angles, as well as image contents with colors, to the incorrect reproduction of which the human eye is particularly sensitive, such as human skin color. Otherwise, the training data should correspond to the usual picture content of a television station.
  • the neural network is adapted by a suitable training algorithm to the best possible reproduction quality, which is influenced by the suitable selection of the training data and the error measure used.
  • the parameters of the neural network are thus automatically adjusted during the training for later use, which is reproduced as realistically as possible using the training data.
  • conventional color decoders use conventional fil- ter structures such as synchronous demodulators including low-pass filters and comb filters, and between them, if necessary, a hard switching controlled by the image content is carried out. With picture contents for which no special switching combination is provided, or with multiple switching processes, picture errors perceived as disturbances can occur.
  • FIG. 1 shows a basic circuit diagram of the neural network for sampling the video signal with 4 times the color carrier frequency
  • FIG. 2 shows an input data mask for the neural network for an NTSC video signal sampled at four times the color carrier frequency
  • Figure 3 shows a corresponding representation for a PAL video signal
  • the neural network 1 in FIG. 1 contains a layer 2 each consisting of an input buffer for an input signal E1,..., E18, a layer 3 consisting of neurons as an output buffer for each output signal Y1, Y2, I ', B' and a layer 4 from neurons with a sigmoid transfer function as a hidden layer.
  • Neurons are usually totalizer amplifiers, to which a number i of signals can be fed on the input side, which are multiplicatively weighted in the neuron with a weighting factor w-j_ and summed with one another and additionally provided with a bias weight.
  • the transfer function of the neuron is one in the case of the output buffer Identity, in the case of the hidden layer neurons a sigmoid function.
  • each neuron of the hidden layer 4 or the output layer 3 is connected to each neuron of the previous layer.
  • Such a structure of a neural network is referred to as a multilayer perceptron.
  • each neuron of the hidden layer 4 has 18 input signals.
  • a neuron for a brightness sample value Y1, Y2 and a neuron for a sample value I ', Q', from which the color signal components I, Q for an NTSC video signal are derived are provided.
  • the video signals I, Q (and U, V for a PAL video signal) are the color difference signals which are modulated as quadrature components onto the color signal carrier in the input-side video signal.
  • an alternative implementation is to provide only a single output neuron for the color signal component C, this color signal component then representing the individual signals I, Q modulated in quadrature.
  • the weighting factors w of the input signals of the neurons of the hidden layer 4 and the weighting factors w 'of the input signals for the neurons of the output layer 3 as well as the respective bias weights are gradually adjusted during the training, so that the error resulting for an input training image of the output signals Y1, Y2, I, Q is as low as possible compared to an ideally decoded reference output signal, taking into account a "flat" error curve.
  • connections and neurons of the hidden layer which are found to be insignificant can be omitted (so-called pruning) in order to reduce the computational effort in the neuronal Reduce network.
  • the sigmoid (S-shaped) transfer function of the neurons of the hidden layer 4 is expediently a tangent hyperbolic (tanh).
  • the neural network can be implemented using analog or digital circuit technology.
  • a processor-based implementation is also expedient, in which the circuit shown in FIG. 1 and its signal flow are simulated in software.
  • application-specific hardware components can be combined with programmable ones in order to implement the network algorithm in hardware.
  • the input signals El, ..., E18 of the neural network 1 are taken from an input mask which is passed step by step over the samples of each image of the coded video signal on the input side.
  • the sample values of the input mask are shown in FIG. 2 for three successive lines.
  • the video signal is color-coded according to the NTSC standard and sampled at four times the color carrier frequency, with the scanning being carried out by 57 ° relative to the color carrier (burst).
  • the sampled values each contain a sum of the brightness signal component Y and one of the color signal components I, Q with a changing sign.
  • the decoded values are calculated for the two middle pixels 20, namely, as shown in FIG.
  • the input mask should be symmetrical with respect to those pixels for which the decoded output signals are calculated, so that as much input information as possible is available to each pixel.
  • the input masks shown in FIG. 1 and the one shown in FIG. 4 have proven to be expedient.
  • the input mask is guided step by step over the input image, the step size being two pixels in the horizontal direction in the case of FIGS. 2 and 3, and one pixel in the horizontal direction in the case of FIG.
  • the input mask jumps to the beginning of the next line.
  • a device 5, 6 for polarity reversal is therefore provided at the outputs 7, 8 for the chrominance signal components of the neural network 1 in FIG. 1, by means of which the sign is changed in each step.
  • the video signal is shifted by 180 ° with a phase angle of 237 ° relative to the color carrier, the signs for the sums between the luminance and chrominance signal components are just reversed.
  • the same neural network and the same control for the Polarity reversers 5, 6 receive negative chrominance signal components I, Q.
  • FIG. 3 shows the corresponding sampling values for sampling a PAL color-coded video signal when sampling with four times the color carrier frequency and a phase angle of 0 ° relative to the color carrier.
  • the V color signal has opposite signs in successive lines in the PAL standard.
  • the assignment of the outputs 7, 8 from U to V to V to U must be reversed when the line changes.
  • the neural network optimally adapts itself organizing to the respective task depending on the respective training process, it is possible to process further input signals, for example differences or sums between diagonally adjacent pixels, which are proportional to the luminance with a constant image content. You can also
  • Input pixels from several successive fields can be used. If sufficient computing capacity is available, instead of the three-layer multilayer perceptron shown in FIG. 1, it is also possible to use neural networks with more than one hidden layer in order to achieve a better approximation quality for higher-order dependencies.
  • the devices 5, 6 shown in FIG. 1 for reversing the sign represent special cases of the above transformation.
  • FIG. 4 A further implementation for the color decoder using a neural network 43 is shown in FIG. 4.
  • the input mask only comprises a central pixel 40 for which a sample value Y for the brightness signal component and a sample value C for the color signal component are calculated.
  • the color signal C is a linear combination of the color difference signals I, Q.
  • the components I, Q are obtained in a conventional manner by synchronous demodulation with orthogonal color carriers F1, F2 and subsequent low-pass filtering in low-pass filters 41, 42. Since standard circuits are already available for this synchronous demodulation and the low-pass filters 41, 42 only have to achieve moderate quality requirements, the use of the neural network 43 results overall in a considerable improvement in quality.
  • the neural network 43 interchangeably fits into conventional circuit concepts in which the Y and C signal component - with the disadvantages specified in the introduction - is calculated by means of a comb filter for a pixel.
  • the input mask of FIG. 4 each has a sample 44, 45 in the horizontal direction in the current image line in addition to the lines above or below it. This provides the network 43 with more horizontal input image information. It is also noteworthy that the input pixel 40 has a completely symmetrical neighborhood in the horizontal direction. In addition, it can be advantageous to supply the neural network with input values which are taken from various fields which follow one another in time, so that the filter effect carried out by the neural network receives a temporal component.
  • radial basis function networks are expedient.

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  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Processing Of Color Television Signals (AREA)

Abstract

Un décodeur couleur destiné à un signal vidéo comporte un réseau neuronal (1) pour fournir séparément la composante signal de chrominance (I, Q) et la composante signal de luminance (Y1, Y2) contenues sous forme de fréquence porteuse dans le signal vidéo. Un perceptron multicouche (1) avec une couche cachée (4) est utilisé avantageusement. Les signaux d'entrée (E1,..., E18) comprennent les pixels d'une grille en soi ainsi que des différences proportionnelles à la composante signal de chrominance. Les signaux de sortie (Y1, Y2, I, Q) sont calculés pour les pixels centraux.
PCT/DE1997/002374 1996-10-21 1997-10-16 Circuit pour le decodage couleur d'un signal video WO1998018264A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE19643388.6 1996-10-21
DE1996143388 DE19643388C1 (de) 1996-10-21 1996-10-21 Schaltungsanordnung zur Farbdekodierung eines Videosignals

Publications (1)

Publication Number Publication Date
WO1998018264A1 true WO1998018264A1 (fr) 1998-04-30

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DE (1) DE19643388C1 (fr)
WO (1) WO1998018264A1 (fr)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1990013978A1 (fr) * 1989-05-09 1990-11-15 Deutsche Itt Industries Gmbh Filtrage en peigne biligne avec melange spatial
JPH03184492A (ja) * 1989-12-14 1991-08-12 Fujitsu Ltd ニューラルネットワークを用いた適応型ｙ／ｃ分離方式
DE19541319A1 (de) * 1994-11-07 1996-05-09 Tektronix Inc Decoder mit adaptivem, nicht-trennbaren digitalen Filter

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1990013978A1 (fr) * 1989-05-09 1990-11-15 Deutsche Itt Industries Gmbh Filtrage en peigne biligne avec melange spatial
JPH03184492A (ja) * 1989-12-14 1991-08-12 Fujitsu Ltd ニューラルネットワークを用いた適応型ｙ／ｃ分離方式
DE19541319A1 (de) * 1994-11-07 1996-05-09 Tektronix Inc Decoder mit adaptivem, nicht-trennbaren digitalen Filter

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
PATENT ABSTRACTS OF JAPAN vol. 15, no. 442 (E - 1131) 11 November 1991 (1991-11-11) *

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DE19643388C1 (de) 1998-07-02

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